Chance constrained sets approximation: A probabilistic scaling approach -- EXTENDED VERSION
Systems and Control
2021-01-19 v2 Systems and Control
Optimization and Control
Abstract
In this paper, a sample-based procedure for obtaining simple and computable approximations of chance-constrained sets is proposed. The procedure allows to control the complexity of the approximating set, by defining families of simple-approximating sets of given complexity. A probabilistic scaling procedure then allows to rescale these sets to obtain the desired probabilistic guarantees. The proposed approach is shown to be applicable in several problem in systems and control, such as the design of Stochastic Model Predictive Control schemes or the solution of probabilistic set membership estimation problems.
Cite
@article{arxiv.2101.06052,
title = {Chance constrained sets approximation: A probabilistic scaling approach -- EXTENDED VERSION},
author = {Martina Mammarella and Victor Mirasierra and Matthias Lorenzen and Teodoro Alamo and Fabrizio Dabbene},
journal= {arXiv preprint arXiv:2101.06052},
year = {2021}
}
Comments
16 pages, 11 figures, submitted to Automatica